1. Field of the Invention
The present invention relates to an apparatus and method of reducing noise in range images, and more particularly, to an apparatus and method of reducing noise in range images by which noise included in range images represented as three-dimensional (3D) coordinate information so that a curved surface can be smoothly represented.
2. Description of the Related Art
Image processing is a basic field in computer vision. Developments in image technologies provide an opportunity for acquiring images in various ways. Processing of three-dimensional (3D) information obtained from images has become an important issue in the field of visualization and vision. Owing to development of 3D sensing technologies, high resolution range images can be generated and thus, noise problem occurs continuously. In other words, a curved surface of interest needs to be extracted from image data including noise. As a result, the necessity for noise reduction methods in 3D image processing has increased.
Image noise reduction or smoothing is generally performed in image processing. Typically, methods of reducing noise in two-dimensional (2D) images are focused on a local value of a quantity field. Furthermore, in order to shape a smooth curved surface of a 3D object, the local geometric relationship is considered. Due to consideration of the local geometric relationship, the 3D object has a stable distribution having a characteristic based on the shape of a curved surface having the same curvature as a curvature of a curved surface of differential geometry.
Curvature is an excellent characteristic that is invariable to transformation for representing a curved surface. Such invariance is a good characteristic for computer vision such as object recognition, position or movement prediction, and image matching. Curvature represents the degree of steepness of a curve or a curved surface and is calculated from first-order and second-order partial differential equations in a 3D Euclid space. However, curvature is very sensitive to noise due to a characteristic based on such differential equations. A curved surface including noise obtained by a 3D image sensor represents non-uniform curvature in the entire region of an observed object. This indicates that most points of the curved surface are very steeply bent. In other words, the curved surface including noise causes curvature that is not uniform even in a flat surface and shows unexpected results. Thus, a steep point and a flat point of the curved surface are not classified. Curvature that is useful for classification is obtained from a smooth curved surface and thus, a method of smoothing the curved surface including noise is required.
Owing to development of vision technologies, outdoor mobile devices for obtaining range images have emerged and thus, range images can be obtained easily and rapidly every time everywhere. However, more and biased noise exists in range images. The magnitude and direction of noise is biased by mobile devices and thus, noise is distributed in different directions. For this reason, an observed curved surface is not uniform, and noise has an anisotropic distribution. A special smoothing method is required for application devices using an on-the-fly based device.
In curvature reconstruction using a radial basis function (RBF) that is generally performed, range data that is scattered when a RBF is obtained is convolved with a smoothing kernel, i.e., a low pass filter and is smoothed. Also, discrete approximation of the smoothing kernel enables an arbitrary filter kernel including an anisotropic and spatially variable filter. Smooth interpolation by moving least squares (MLS) approximation is also a powerful method. A mesh independent MLS-based projection method for general curved surface interpolation has been proposed. The mesh independent MLS-based projection method can be applied to (d−1)-dimensional manifold when d≧2, and thus, a curved surface is C∞ smooth. Meanwhile, image smoothing by diffusion is general, and a curved surface smoothing method using anisotropic diffusion of a level set curved surface model and a normal vector is better than isotropic diffusion that is performed as a low pass filter in noise reduction.
However, range images that are obtained by an on-the-fly-based 3D imaging device include more noise than general 3D images and have various noise levels in the same frame. Thus, the above-described methods are not appropriate to application on on-the-fly-based range images. Thus, smoothing by which noise in range images can be effectively reduced is required.